Identifying Risk Factors for Recent HIV
Infection in Kenya Using a Recent Infection
Testing Algorithm: Results from a Nationally
Representative Population-Based Survey
Andrea A. Kim1*, Bharat S. Parekh2, Mamo Umuro3, Tura Galgalo3, Rebecca Bunnell1, Ernest Makokha1, Trudy Dobbs2, Patrick Murithi4, Nicholas Muraguri5, Kevin M. De Cock6, Jonathan Mermin6, 2007 KAIS study group¶
1US Centers for Disease Control and Prevention (CDC), Center for Global Health (CGH), Division of Global HIV and Tuberculosis (DGHT), Nairobi, Kenya,2US CDC, CGH, DGHT, Atlanta, Georgia, United States of America,3Kenya Ministry of Health, National Public Health Laboratory Services, Nairobi, Kenya,4Kenya Ministry of Health, National AIDS Control Council, Nairobi, Kenya,5Kenya Ministry of Health, National AIDS and STI Control Programme, Nairobi, Kenya,6US CDC, CGH, Nairobi, Kenya
¶ Membership of the 2007 KAIS Study Group is provided in the Acknowledgments.
*aakim@cdc.gov
Abstract
Introduction
A recent infection testing algorithm (RITA) that can distinguish recent from long-standing HIV infection can be applied to nationally representative population-based surveys to char-acterize and identify risk factors for recent infection in a country.
Materials and Methods
We applied a RITA using the Limiting Antigen Avidity Enzyme Immunoassay (LAg) on stored HIV-positive samples from the 2007 Kenya AIDS Indicator Survey. The case defini-tion for recent infecdefini-tion included testing recent on LAg and having no evidence of antiretrovi-ral therapy use. Multivariate analysis was conducted to determine factors associated with recent and long-standing infection compared to HIV-uninfected persons. All estimates were weighted to adjust for sampling probability and nonresponse.
Results
Of 1,025 HIV-antibody-positive specimens, 64 (6.2%) met the case definition for recent infection and 961 (93.8%) met the case definition for long-standing infection. Compared to HIV-uninfected individuals, factors associated with higher adjusted odds of recent infection were living in Nairobi (adjusted odds ratio [AOR] 11.37; confidence interval [CI] 2.64–48.87) and Nyanza (AOR 4.55; CI 1.39–14.89) provinces compared to Western province; being widowed (AOR 8.04; CI 1.42–45.50) or currently married (AOR 6.42; CI 1.55–26.58) com-pared to being never married; having had2 sexual partners in the last year (AOR 2.86; CI
a11111
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Citation:Kim AA, Parekh BS, Umuro M, Galgalo T, Bunnell R, Makokha E, et al. (2016) Identifying Risk Factors for Recent HIV Infection in Kenya Using a Recent Infection Testing Algorithm: Results from a Nationally Representative Population-Based Survey. PLoS ONE 11(5): e0155498. doi:10.1371/journal. pone.0155498
Editor:Charlene S. Dezzutti, University of Pittsburgh, UNITED STATES
Received:September 14, 2015
Accepted:May 1, 2016
Published:May 19, 2016
Copyright:This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under theCreative Commons CC0public domain dedication.
Data Availability Statement:Data are from the Kenya AIDS Indicator Survey (KAIS) which are owned by the Government of Kenya. For parties that are interested in conducting secondary analysis of the KAIS dataset, permission to access KAIS data can be requested through submission of a KAIS Analysis and Publication Concept Note to the National AIDS and STI Control Program (NASCOP) of the Kenya Ministry of Health (email:
1.51–5.41); not using a condom at last sex in the past year (AOR 1.61; CI 1.34–1.93); reporting a sexually transmitted infection (STI) diagnosis or symptoms of STI in the past year (AOR 1.97; CI 1.05–8.37); and being aged<30 years with: 1) HSV-2 infection (AOR 8.84; CI 2.62–29.85), 2) male genital ulcer disease (AOR 8.70; CI 2.36–32.08), or 3) lack of male circumcision (AOR 17.83; CI 2.19–144.90). Compared to HIV-uninfected persons, fac-tors associated with higher adjusted odds of long-standing infection included living in Coast (AOR 1.55; CI 1.04–2.32) and Nyanza (AOR 2.33; CI 1.67–3.25) provinces compared to Western province; being separated/divorced (AOR 1.87; CI 1.16–3.01) or widowed (AOR 2.83; CI 1.78–4.45) compared to being never married; having ever used a condom (AOR 1.61; CI 1.34–1.93); and having a STI diagnosis or symptoms of STI in the past year (AOR 1.89; CI 1.20–2.97). Factors associated with lower adjusted odds of long-standing infection included using a condom at last sex in the past year (AOR 0.47; CI 0.36–0.61), having no HSV2-infection at aged<30 years (AOR 0.38; CI 0.20–0.75) or being an uncircumcised male aged<30 years (AOR 0.30; CI 0.15–0.61).
Conclusion
We identified factors associated with increased risk of recent and longstanding HIV infection using a RITA applied to blood specimens collected in a nationally representative survey. Though some false-recent cases may have been present in our sample, the correlates of recent infection identified were epidemiologically and biologically plausible. These methods can be used as a model for other countries with similar epidemics to inform targeted combi-nation prevention strategies aimed to drastically decrease new infections in the population.
Introduction
Over the past decade, substantial scale-up of HIV care and treatment programs globally has resulted in approximately 16 million people accessing HIV treatment by 2015 [1]. Yet antire-troviral treatment (ART) coverage has not reached universal access in most resource-con-strained countries, and other HIV prevention efforts have not sufficiently reduced HIV transmission and new infections. Sub-Saharan Africa remains disproportionately affected by the HIV epidemic, accounting for 70% of HIV infections worldwide, 70% of new HIV infec-tions among adults, and 86% of new HIV infecinfec-tions among children [1]. With the implementa-tion of large-scale combinaimplementa-tion prevenimplementa-tion strategies, including voluntary male medical circumcision and intensified access to treatment [2–7], accurate tools to monitor where and among whom new HIV infections are occurring are essential to evaluate the impact of these strategies and to improve the efficiency of targeted prevention programs.
There are several approaches for measuring the occurrence of new HIV infections, the gold standard being measurement through prospective cohorts that track HIV seroconversion among HIV-uninfected persons. The most common approach in developing nations, however, has been through inference, using trends in HIV prevalence and assumptions about HIV-spe-cific mortality and the impact of antiretroviral therapy (ART) coverage on survival [8–11]. While these indirect measures have been useful in estimating HIV incidence across important demographic strata, they cannot provide information beyond these basic metrics, limiting their utility for targeted programming.
supporting information. Once submitted, concept notes will be reviewed by the KAIS Analysis and Review Committee which is chaired by NASCOP and a final decision will be made within one month of submission.
Funding:This study was funded through the US President’s Emergency Plan for AIDS Relief. The funders had no role in study design, data collection, data analysis, decision to publish, or manuscript preparation.
Competing Interests:As an inventor of the LAg-Avidity EIA and as per policy of U.S. government, BSP receives royalties from the sale of this assay. There are no patents relating to this assay and there are no further products in development or marketed products to declare. This does not alter the authors’
Several groups have developed laboratory-based approaches to distinguish recently acquired HIV infections from HIV infections of longer duration in cross-sectional surveys [12–14]. HIV incidence assays are based on the underlying principle that the immunological response to HIV infection evolves for a number of months following infection, allowing for the identifica-tion of immunological biomarkers of early HIV disease progression that can serve as indicators of recent infection. If accurate, incidence assays can provide a rapid and cost-effective approach for providing reliable and up-to-date information on the dynamics of HIV transmission for more effective planning [15]. The Limiting Antigen Avidity Enzyme Immunoassay (hereafter termed“LAg”) has been previously described as a tool for estimating population-level HIV incidence in settings world-wide [16–18]. Recent infection testing algorithms (RITA) that com-bine the results of an HIV incidence assay with markers of chronic infection to confirm recent infection (e.g., antiretroviral therapy use and viral suppression), is the recommended approach for distinguishing recent from long-standing infection in a survey setting [19].
As we move closer towards substantially reversing the HIV epidemic, countries are being called to evaluate the public health impact of intensified scale-up of combination prevention strategies on the prevention of new HIV infections in the population. To demonstrate the util-ity of RITAs to inform such strategies, we applied a LAg-based RITA to blood specimens col-lected in a nationally representative serologic survey in Kenya to identify factors associated with recent infection in 2007. These methods were designed to be replicable in countries with similar epidemics and epidemiological data sources to provide insight into sub-populations most vulnerable to incident infection for targeted prevention.
Materials and Methods
Study design
The 2007 Kenya AIDS Indicator Survey was a two-stage cluster household survey that pro-duced nationally representative estimates of HIV indicators in Kenya [20]. Participants aged 15–64 years consented to a questionnaire on demographics; behavior; knowledge and attitudes towards HIV/AIDS; and access to care, treatment, and prevention programs for HIV infection. Blood specimens were collected to test for HIV-1 antibodies; CD4 cell counts using Becton, Dickinson and Company (BD) FACSCalibur flow cytometer (BD Biosciences, San Jose, CA), if found to be HIV infected; HSV-2 testing using Kalon HSV Type 2-specific IgG enzyme immu-noassay (Kalon Biologicals, Guildford, United Kingdom); and syphilis testing using the Trepo-nema pallidum particle agglutination assay (Serodia-TPPA, Fuirebio Diagnosis Inc, Tokyo, Japan) as the screening assay and confirmed using the rapid plasma regain test (BD, Franklin Lakes, New Jersey, USA) on undiluted serum specimens to test for reaginic antigens. All testing was conducted in a central laboratory in Nairobi, and results were returned to participants approximately 6 weeks after blood collection. Consent was requested from participants for per-mission to store remnant blood specimens in -70°C freezers at the central laboratory for future unspecified testing. Participants were informed that test results from future tests would not be returned.
Laboratory methods
Approximately seven years after data collection, HIV-antibody positive dried blood spot (DBS) specimens were retrieved from freezer storage and tested for HIV-1 RNA concentra-tion (Abbott M2000 Real-Time HIV-1 Assay, Abbott Laboratories, Abbott Park, IL). Viral load testing resulted in undetectable levels of HIV-1 RNA in over 90% of samples tested. While the lack of viral RNA was suggestive of nucleic acid degradation over the seven year storage period we were unable to confirm this with additional laboratory analysis. As such, viral load testing data were not included in this analysis. Following viral load testing, remain-ing DBS specimens were transported to the University of Cape Town in South Africa where a qualitative ARV drug assay was applied to specimens to test for the presence of four antire-troviral (ARV) drugs, including nevirapine (NVP), efavirenz (EFV), lamivudine (3TC), and lopinavir (LPV). At the time of the survey, the first-line standardized national ARV drug reg-imen was stavudine (d4T) or zidovudine (AZT) + 3TC + EFV or NVP, and the second line regimen was didanosine (ddI) + abacavir (ABC) + lopinavir/ritonavir (LPV/r) or tenofovir (TDF) + ABC + LPV/r [22].
ARV drug testing was conducted using a qualitative ARV assay using liquid chromatogra-phy-tandem mass spectrometry (MS), a method applied previously in national household sur-veys in South Africa [23]. The method consisted of incubating the DBS punches (2 x 3.0 mm) in water for 10 minutes, followed by protein precipitation using Acetonitrile and by high per-formance liquid chromatography with MS/MS detection. Efavirenz-d5, nevirapine-d3, lopina-vir-d8 and lamivudine-15N2,13C were used as the internal standards. Chromatographic
separation was achieved using a Phenomenex Luna 5μPFP (2) 50 x 2.00 mm analytical
col-umn. An AB Sciex API 4000 mass spectrometer at unit resolution using the Scheduled MRM™ algorithm was used to monitor the transition of the protonated precursor ions m/z 316.0 and m/z 321.0 to the product ions m/z 244.0 and m/z 246.0 for efavirenz and efavirenz-d5, m/z 266.9 and m/z 270. 0 to the product ions m/z 198.2 and m/z 229.1 for nevirapine and nevira-pine-d3, m/z 629.4 and m/z 637.4 to the product ions m/z 120.2 and m/z 191.2 for lopinavir and lopinavir-d8, m/z 230.2 and m/z 233.2 to the product ions m/z 112.0 and m/z 115.2 for lamivudine and lamivudine-15N2,13C, respectively. A cut-off standard with a concentration of
0.020μg/ml was verified against regressions constructed using calibration standards for each
analyte. A specimen with levels observed greater or equal to 0.020μg/ml was designated as
positive for the analyte while levels below 0.020μg/ml were deemed negative for the analyte.
Specimens that tested positive for one or more analytes were classified as having the ARV biomarker.
Classification of recent and long-standing infection
Data analysis
Descriptive analyses were conducted to determine the proportion of recent and long-standing infection by select demographic, behavioral, and biological characteristics. The Kruskal-Wallis test was used to test for differences in medians. Variables associated with recent and long-standing infection at a significance level of p-value<0.2 in bivariate analysis and those known to be risk factors for HIV infection or suspected confounders were included in multinomial logistic regression models which used an ordinal scale of the outcome variable where HIV uninfected was classified as outcome category = 1, serving as the referent group; long-standing infection classified as outcome category = 2; and recent infection classified as outcome cate-gory = 3 [27]. This categorization allowed the model to identify independent and significant risk factors for recent and long-standing infection simultaneously allowing for direct compari-son of the magnitude of the measure of associations reported for recent infection and long-standing infection. A separate multivariate logistic regression model was applied to directly compare recent and longstanding infection with respect to demographic, behavioral, and bio-logical factors. Missing categories were created to account for missing data for female partici-pants for male-specific variables (e.g., male circumcision and presence of male genital sores or ulcers) and male participants for female-specific variables (e.g., current pregnancy based on self-report). Adjusted odds ratios (AOR) and 95% confidence intervals (CI) were presented for the final estimates in the models. Associations with 95% CIs that did not include 1.0 were considered significantly associated with recent or long-standing infection. Data analysis was conducted in SAS version 9.3 (SAS Institute, Cary, NC, USA). All analyses were weighted to account for the complex survey design and adjusted for survey nonresponse.
This study was reviewed and approved by the institutional review boards (IRBs) at the Kenya Medical Research Institute (KEMRI) and the US Centers for Disease Control and Pre-vention (CDC). As standard practice for surveys in Kenya, verbal informed consent with a signature of the interviewer as documentation of consent was obtained for adult participants aged 18–64 years. For adolescents aged 15–17 years, a second signature of the interviewer was obtained documenting assent of the participating minor and consent of the parent or guardian. A waiver of documentation of informed consent under 45 CF4 46.117 was approved by the KEMRI and CDC IRBs.
Results
In the 2007 KAIS, 1,081 of 15,583 survey participants with blood specimens for HIV testing were HIV-antibody-positive, and 1,025 (94.8%) of these were available for LAg testing. A total of 77 (7.5%) tested recent and 948 (92.5%) tested non-recent on LAg (Fig 1). Of the 77 speci-mens that tested recent, 13 were on ART (9 confirmed by ARV drug testing on 51 specispeci-mens and 4 confirmed by self-report for 26 specimens not tested for ARV drugs) and were re-defined as long-standing infection in the final analysis. Among HIV-positive specimens tested, the final number of that met the case definition for recent and long-standing infection was 64 (6.2%) and 961 (93.8%), respectively.
Characterization of recent infection
in the youngest age groups, with 43.0% of recent infection among women aged 15–24 years and 41.6% among those aged 25–34 years. Among men, recent infections were highest in those aged 25–34 years, at 51.0%. A large proportion of recent infections were among participants from rural areas (76.3%) and among persons living in Rift Valley (34.4%) and Nyanza (23.9%) provinces.
Most recent infections were among persons in married or cohabiting relationships (83.7%) compared to persons who were never married or cohabiting (8.8%), widowed (5.0%), or sepa-rated or divorced (2.5%). Among persons with recent infection who were in a married or cohabiting relationship, 16.7% of men and 4.8% of women reported having sexual partners outside of the relationship in the year preceding the survey. Twenty-nine persons with recent infection had linked HIV test results with a spouse in the household. Among those, 0/29 (0%) had a spouse with recent infection, 9/29 (31.0%) had a spouse with long-standing infection, and 20/29 (69.0%) had a spouse who was HIV uninfected. Among 169 married couples who had sero-discordant HIV results in the survey, 12.9% of infected men and 13.7% of HIV-infected women had recent HIV infection. Among HIV-positive sero-concordant couples (n = 98), 3.6% of HIV-infected men and 4.3% of HIV-infected women had recent infections.
Most recent infections (63.5%) occurred among persons who had never had an HIV test, followed by persons who believed they were HIV-negative based on their last HIV test result (35.3%) but tested HIV-positive in the survey. Among the latter group, 48.2% reported Fig 1. HIV-antibody-positive specimens in the 2007 Kenya AIDS Indicator Survey.
Table 1. Frequencies and weighted proportions of select demographic, behavioral, and biologic variables among HIV-infected women and men aged 15–64 years by recent and long-standing infection status, 2007 Kenya AIDS Indicator Survey.
Recent Infection1 Long-standing Infection2
Total (N = 64) Men (N = 27) Women (N = 37) Total (N = 961) Men (N = 307) Women (N = 654)
N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI)
Select variables
Median CD4+ T-cell count (IQR)3 952 (609
– 1,243)
888 (597– 1,133)
977 (670– 1,283)
483 (292– 721)
435 (264– 680)
508 (304– 734)
Sex
Men 27 38.1 (22.6–
53.7)
- - - - 307 30.6 (27.5–
33.8)
- - -
-Women 37 61.9 (46.3–
77.4)
- - - - 654 69.4 (66.2–
72.5)
- - -
-Age category (years)
15–24 18 29.1 (17.4–
40.9)
3 6.6 (0–15.1) 15 43.0 (25.7– 60.3)
175 16.3 (13.7– 19.0)
26 7.5 (4.3– 10.6)
149 20.3 (16.6– 24.0)
25–34 26 45.2 (29.8–
60.5)
12 51.0 (25.3– 76.7)
14 41.6 (23.9– 59.2)
344 36.5 (33.1– 39.8)
99 34.2 (28.2– 40.2)
245 37.5 (33.3– 41.7)
35–44 9 12.8 (3.5–
22.2)
4 17.9 (0– 37.2)
5 9.8 (0.7– 18.8)
250 27.7 (24.1– 31.3)
103 35.1 (28.7– 41.6)
147 24.5 (20.6– 28.4)
45–54 7 7.0 (1.5–
12.6)
5 11.7 (0– 23.5)
2 4.1 (0–9.9) 155 15.6 (13.0– 18.1)
62 18.2 (13.4– 22.9)
93 14.4 (11.4– 17.5)
55–64 4 5.9 (0–12.2) 3 12.8 (0–
28.6)
1 1.6 (0–4.6) 37 3.9 (2.5–5.3) 17 5.1 (2.5–7.7) 20 3.4 (1.7–5.0)
Education
No primary 11 16.9 (1.3–
32.5)
4 6.4 (0–14.1) 7 23.4 (0.6– 46.1)
94 10.9 (8.0– 13.8)
12 4.9 (1.5–8.3) 82 13.6 (9.8– 17.4) Incomplete primary 27 45.4 (29.0–
61.8)
10 50.3 (24.5– 76.1)
17 42.4 (21.9– 62.9)
315 32.8 (29.0– 36.7)
80 25.7 (19.8– 31.6)
235 36.0 (31.2– 40.8)
Complete primary 11 15.2 (6.0–
24.5)
5 17.2 (0– 34.9)
6 14.0 (2.7– 25.4)
280 28.7 (25.4– 32.0)
91 29.2 (23.0– 35.4)
189 28.5 (24.5– 32.4)
Secondary+4 15 22.4 (9.4
– 35.5)
8 26.1 (5.3– 46.9)
7 20.2 (4.1– 36.2)
272 27.5 (23.6– 31.4)
124 40.2 (33.6– 46.8)
148 22.0 (17.6– 26.3)
Wealth index5
Lowest 15 24.2 (8.4–
40.1)
5 15.7 (0.9– 30.5)
10 29.5 (7.4– 51.6)
159 16.8 (13.5– 20.1)
46 15.3 (10.5– 20.2)
113 17.5 (13.8– 21.1)
Second 12 17.3 (6.9–
27.7)
6 19.1 (0– 39.5)
6 16.2 (3.9– 28.5)
190 20.1 (16.9– 23.4)
57 21.2 (15.9– 26.5)
133 19.7 (16.2– 23.1)
Middle 10 19.6 (6.1–
33.1)
3 15.7 (0– 34.6)
7 22.0 (5.0– 39.0)
173 17.8 (14.9– 20.8)
59 17.9 (12.9– 23.0)
114 17.8 (14.5– 21.1)
Fourth 13 15.5 (6.2–
24.8)
5 14.1 (1.6– 26.6)
8 16.4 (4.4– 28.4)
189 20.8 (17.1– 24.5)
54 17.6 (12.4– 22.7)
135 22.2 (17.8– 26.6)
Highest 14 23.3 (7.8–
38.9)
8 35.5 (6.5– 64.5)
6 15.9 (2.0– 29.7)
250 24.4 (19.8– 29.0)
91 28.0 (21.3– 34.7)
159 22.9 (18.0– 27.7)
Residence
Urban 15 23.7 (8.1–
39.2)
8 33.8 (4.6– 63.0)
7 17.4 (3.2– 31.6)
299 28.0 (23.5– 32.6)
97 25.5 (19.3– 31.8)
202 29.2 (23.7– 34.6)
Rural 49 76.3 (60.8–
91.9)
19 66.2 (37.0– 95.4)
30 82.6 (68.4– 96.8)
662 72.0 (67.4– 76.5)
210 74.5 (68.2– 80.7)
452 70.8 (65.4– 76.3)
Province
Nairobi 10 13.8 (2.8–
24.8)
5 12.8 (0– 28.9)
5 14.4 (0.8– 28.0)
127 12.5 (8.8– 16.1)
42 12.1 (6.7– 17.4)
85 12.6 (8.5– 16.8)
Central 5 7.8 (0.9–
14.7)
0 - 5 12.6 (1.6–
23.6)
61 6.8 (3.9–9.6) 22 8.6 (3.4– 13.7)
39 5.9 (3.5–8.4)
Table 1. (Continued)
Recent Infection1 Long-standing Infection2
Total (N = 64) Men (N = 27) Women (N = 37) Total (N = 961) Men (N = 307) Women (N = 654)
N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI)
Coast 9 7.7 (2.1–
13.3)
5 11.0 (0.8– 21.2)
4 5.7 (0–11.6) 115 9.1 (6.7– 11.5)
37 8.3 (5.1– 11.5)
78 9.4 (6.4– 12.5) Eastern 4 7.5 (0–15.1) 2 9.8 (0–24.5) 2 6.1 (0–14.5) 95 11.2 (7.6–
14.8)
18 6.9 (3.4– 10.4)
77 13.1 (8.7– 17.6) North Eastern 1 0.3 (0.2–0.4) 1 0.8 (0–2.4) 0 - 6 0.2 (0–0.5) 3 0.3 (0–0.8) 3 0.2 (0–0.4)
Nyanza 18 23.9 (12.7–
35.1)
6 20.6 (4.4– 36.8)
12 25.9 (10.7– 41.0)
319 32.3 (28.2– 36.3)
105 33.8 (27.5– 40)
214 31.6 (27.3– 35.9)
Rift Valley 13 34.4 (14.0–
54.8)
7 42.2 (12.3– 72.2)
6 29.6 (5.1– 54.1)
135 18.9 (14.8– 22.9)
47 19.6 (14.7– 24.5)
88 18.5 (13.8– 23.3) Western 4 4.6 (0–9.4) 1 2.7 (0–8.2) 3 5.7 (0–12.6) 103 9.1 (6.7–
11.5)
33 10.5 (6.2– 14.8)
70 8.5 (6.0– 10.9)
Marital status
Never married/cohabiting 7 8.8 (1.9– 15.7)
2 3.6 (0–9.4) 5 12.0 (1.0– 22.9)
123 12.3 (9.7– 14.8)
43 12.1 (8.5– 15.8)
80 12.3 (8.9– 15.8) Separated/divorced 3 2.5 (0–5.9) 1 0.4 (0–1.1) 2 3.8 (0–9.2) 107 11.1 (8.5–
13.8)
18 5.5 (2.6–8.3) 89 13.6 (10.1– 17.1) Widowed 4 5.0 (0–10.1) 1 2.5 (0–7.5) 3 6.5 (0–14.1) 139 15.5 (12.6–
18.4)
15 5.2 (2.7–7.8) 124 20.0 (16.3– 23.8) Currently married/cohabiting 50 83.7 (74.7–
92.8)
23 93.5 (85.6– 100)
27 77.7 (63.7– 91.7)
592 61.1 (57.0– 65.2)
231 77.2 (71.9– 82.4)
361 54.0 (49.1– 59.0)
Currently pregnant
No 33 90.7 (81.7–
99.8)
- - 33 90.7 (81.7– 99.8)
611 94.0 (92.2– 95.8)
- - 611 94.0 (92.2– 95.8)
Yes 4 9.3 (0.2–
18.3)
- - 4 9.3 (0.2–
18.3)
43 6.0 (4.2–7.8) - - 43 6.0 (4.2–7.8)
No. sex partners in past 12 months
0–1 54 82.4 (69.0–
95.8)
18 58.5 (30.4– 86.6)
36 97.1 (91.5– 100)
870 91.7 (90.0– 93.4)
244 80.2 (75.6– 84.8)
626 96.8 (95.4– 98.1)
2+ 10 17.6 (4.2–
31.0)
9 41.5 (13.4– 69.6)
1 2.9 (0–8.5) 86 8.3 (6.6– 10.0)
63 19.8 (15.2– 24.4)
23 3.2 (1.9–4.6)
Ever used a condom
No 45 78.4 (66.4–
90.5)
18 72.9 (53.4– 92.4)
27 82.0 (66.5– 97.5)
553 60.4 (56.6– 64.1)
167 57.9 (51.0– 64.7)
386 61.5 (57.1– 65.8)
Yes 16 21.6 (9.5–
33.6)
8 27.1 (7.6– 46.6)
8 18.0 (2.5– 33.5)
391 39.6 (35.9– 43.4)
133 42.1 (35.3– 49.0)
258 38.5 (34.2– 42.9)
Condom use at last sex in past 12 months
Used a condom 3 7.8 (0–21.3) 2 19.6 (0–
50.4)
1 0.3 (0–0.8) 145 15.8 (12.8– 18.7)
59 19.8 (15.0– 24.5)
86 14.0 (10.6– 17.4) Did not use a condom 58 92.2 (78.7–
100)
24 80.4 (49.6– 100)
34 99.7 (99.2– 100)
799 84.2 (81.3– 87.2)
241 80.2 (75.5– 85.0)
558 86.0 (82.6– 89.4)
Unprotected sex at last sex with partner of unknown of discordant HIV status in past 12 months
No 5 12.0 (0–
27.0)
3 22.7 (0– 53.2)
2 4.0 (0–11.4) 167 24.0 (20.1– 27.9)
68 26.3 (20.7– 31.8)
99 22.7 (17.7– 27.6)
Yes 50 88.0 (73.0–
100)
23 77.3 (46.8– 100)
27 96.0 (88.6– 100)
576 76.0 (72.1– 79.9)
201 73.7 (68.2– 79.3)
375 77.3 (72.4– 82.3)
Ever tested for HIV
No 40 63.5 (47.6–
79.3)
19 68.3 (39.5– 97.0)
21 60.3 (42.1– 78.5)
511 54.2 (50.2– 58.3)
189 64.6 (58.4– 70.7)
322 49.7 (44.7– 54.6)
Yes 23 36.5 (20.7–
52.4)
8 31.7 (3.0– 60.5)
15 39.7 (21.5– 57.9)
442 45.8 (41.7– 49.8)
116 35.4 (29.3– 41.6)
326 50.3 (45.4– 55.3)
Table 1. (Continued)
Recent Infection1 Long-standing Infection2
Total (N = 64) Men (N = 27) Women (N = 37) Total (N = 961) Men (N = 307) Women (N = 654)
N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI) N % (95% CI)
Self-reported HIV status based on last HIV test result
HIV-positive 1 1.2 (0–3.6) 1 3.0 (0–9.0) 0 - 154 17.9 (14.3–
21.4)
45 15.4 (10.5– 20.4)
109 18.9 (14.8– 23.0)
HIV-negative 22 35.3 (19.6–
51.1)
7 28.7 (0– 57.7)
15 39.7 (21.5– 57.9)
272 27.1 (23.8– 30.3)
63 18.0 (13.3– 22.8)
209 31.0 (27.0– 34.9) Unknown, never tested 40 63.5 (47.6–
79.3)
19 68.3 (39.5– 97.0)
21 60.3 (42.1– 78.5)
511 55.1 (51.0– 59.2)
189 66.5 (60.3– 72.8)
322 50.1 (45.2– 55.1)
Reported sexually transmitted infection diagnosis or symptoms in past 12 months
No 57 85.6 (71.9–
99.4)
21 67.3 (38.1– 96.5)
36 97.0 (91.0– 100)
892 92.8 (90.8– 94.8)
271 87.7 (83.6– 91.8)
621 95.1 (93.0– 97.1)
Yes 7 14.4 (0.6–
28.1)
6 32.7 (3.5– 61.9)
1 3.0 (0–9.0) 69 7.2 (5.2–9.2) 36 12.3 (8.2– 16.4)
33 4.9 (2.9–7.0)
HSV-2 result
Negative 36 59.3 (45.3–
73.4)
19 77.0 (59.8– 94.2)
17 48.5 (31.8– 65.1)
153 15.6 (12.9– 18.3)
64 20.5 (15.3– 25.6)
89 13.4 (10.3– 16.6)
Positive 28 40.7 (26.6–
54.7)
8 23.0 (5.8– 40.2)
20 51.5 (34.9– 68.2)
807 84.4 (81.7– 87.1)
243 79.5 (74.4– 84.7)
564 86.6 (83.4– 89.7)
Syphilis result
Negative 63 97.2 (91.8–
100)
26 92.7 (78.8– 100)
37 100 921 95.6 (93.9– 97.3)
285 93.7 (90.5– 96.9)
636 96.4 (94.3– 98.5) Positive 1 2.8 (0–8.2) 1 7.3 (0–21.2) 0 - 37 4.4 (2.7–6.1) 20 6.3 (3.1–9.5) 17 3.6 (1.5–5.7)
Genital sore or ulcer on or near the penis in past 12 months
No 25 78.0 (47.7–
100)
25 78.0 (47.9– 100)
- 286 93.4 (90.3– 96.4)
286 93.4 (90.3– 96.4)
-
-Yes 2 22.0 (0–
52.3)
2 22.0 (0– 52.1)
- 21 6.6 (3.6–9.7) 21 6.6 (3.6–9.7) -
-Male circumcision status
Circumcised 4 10.9 (0–
22.8)
4 10.9 (0– 22.8)
- 133 42.2 (35.3– 49.0)
133 42.2 (35.3– 49.0)
-
-Not circumcised 23 89.1 (77.2–
100)
23 89.1 (77.2– 100)
- 174 57.8 (51.0– 64.7)
174 57.8 (51.0– 64.7)
-
-1Specimens were defined as being from persons with recent HIV infection if they tested recent on LAg and were not from HIV-infected persons who were
on ART. Recent infection was presumed to represent HIV infection acquired less than one year prior to specimen collection.
2Specimens were defined as being from persons with long-standing HIV infection if they: 1) tested non-recent on LAg or 2) tested recent on LAg and were
from HIV-infected persons who were on ART. Long-standing infection was presumed to represent HIV infection acquired one year or greater prior to specimen collection.
3Based on results from 860 (84%) of 1,025 HIV-positive specimens with CD4+ T-cell count results. 4Secondary+ includes any years of secondary schooling whether completed or not.
5The wealth index was a composite measure of the living standard of a household, calculated using data on a household’s ownership of selected assets,
materials used for houshing construction, water access and sanitation facitlities. The wealth index placed household on a contniuous scale of relative weath using principal components anlysis. Individuals were ranked according to the score of the household in which they resided and the sample was divisded intofive groups, each with an equal number of indiviuals (quintiles), ranging from the lowest to highest level of wealth.
Table 2. Frequencies and weighted proportions of select demographic, behavioral, and biologic variables among HIV-uninfected women and men aged 15–64 years, 2007 Kenya AIDS Indicator Survey.
HIV-uninfected
Total (N = 14,772) Men (N = 6,449) Women (N = 8,323)
N % (95% CI) N % (95% CI) N % (95% CI)
Select variables
Sex
Men 6449 43.4 (42.3–
44.4)
- - -
-Women 8323 56.6 (55.6–
57.7)
- - -
-Age category (years)
15–24 4932 34.8 (33.6–
35.9)
2176 35.6 (34.0– 37.3)
2756 34.1 (32.8– 35.4)
25–34 3752 24.5 (23.4–
25.6)
1530 22.6 (21.3– 24.0)
2222 26.0 (24.6– 27.3)
35–44 2670 18.4 (17.6–
19.3)
1138 18.0 (16.8– 19.3)
1532 18.7 (17.7– 19.8)
45–54 2060 13.2 (12.5–
14.0)
905 13.0 (12.0– 14.0)
1155 13.4 (12.6– 14.2)
55–64 1358 9.1 (8.4–9.7) 700 10.7 (9.8–
11.6)
658 7.8 (7.1–8.5)
Education
No primary 2106 11.4 (10.3–
12.6)
591 6.8 (5.6–8.1) 1515 15.0 (13.6– 16.4)
Incomplete primary 4143 29.5 (27.9–
31.1)
1795 29.6 (27.5– 31.6)
2348 29.4 (27.8– 31.0)
Complete primary 3487 24.1 (23.0–
25.3)
1526 23.8 (22.3– 25.2)
1961 24.4 (23.0– 25.8)
Secondary+1 5036 35.0 (32.9
– 37.0)
2537 39.8 (37.5– 42.2)
2499 31.2 (29.1– 33.3)
Wealth index2
Lowest 2571 15.4 (13.5–
17.2)
1081 15.4 (13.3– 17.5)
1490 15.4 (13.6– 17.2)
Second 2702 17.7 (16.0–
19.5)
1173 18.0 (16.1– 20.0)
1529 17.5 (15.8– 19.2)
Middle 2855 19.7 (18.2–
21.3)
1241 19.6 (17.9– 21.3)
1614 19.9 (18.2– 21.5)
Fourth 2880 21.2 (19.4–
23.0)
1277 21.4 (19.3– 23.5)
1603 21.0 (19.2– 22.9)
Highest 3764 26.0 (23.1–
28.9)
1677 25.7 (22.8– 28.5)
2087 26.2 (23.1– 29.3)
Residence
Urban 3599 22.9 (19.8–
26.0)
1589 22.3 (19.4– 25.3)
2010 23.4 (20.0– 26.8)
Rural 11173 77.1 (74.0–
80.2)
4860 77.7 (74.7– 80.6)
6313 76.6 (73.2– 80.0)
Province
Nairobi 1673 9.2 (8.0–
10.5)
746 8.8 (7.3– 10.3)
927 9.6 (8.2– 11.0)
Central 2200 14.7 (13.3–
16.0)
961 14.5 (13.0– 15.9)
1239 14.8 (13.3– 16.3)
Coast 1646 7.7 (6.5–8.9) 704 7.7 (6.4–8.9) 942 7.7 (6.4–9.1)
Table 2. (Continued)
HIV-uninfected
Total (N = 14,772) Men (N = 6,449) Women (N = 8,323)
N % (95% CI) N % (95% CI) N % (95% CI)
Eastern 2449 18.1 (16.2–
19.9)
1072 18.4 (16.0– 20.7)
1377 17.8 (16.1– 19.5)
North Eastern 746 2.0 (1.7–2.3) 315 2.0 (1.6–2.4) 431 2.0 (1.7–2.3)
Nyanza 2023 14.1 (12.5–
15.6)
875 14.2 (12.3– 16.1)
1148 14.0 (12.5– 15.5)
Rift Valley 2107 22.3 (19.4–
25.1)
944 22.5 (19.9– 25.2)
1163 22.0 (18.8– 25.3)
Western 1928 12.0 (10.6–
13.4)
832 11.9 (10.5– 13.4)
1096 12.1 (10.5– 13.6)
Marital status
Never married/cohabiting 4386 30.1 (28.8–
31.3)
2407 38.4 (36.7– 40.1)
1979 23.7 (22.3– 25.2)
Separated/divorced 806 5.4 (7.8–
12.6)
268 4.2 (3.6–4.8) 538 6.3 (5.6–6.9)
Widowed 623 4.4 (11.9–
17.30)
72 1.3 (0.9–1.7) 551 6.8 (6.2–7.5)
Currently married/cohabiting 8957 60.1 (58.8–
61.4)
3702 56.1 (54.4– 57.9)
5255 63.2 (61.7– 64.7)
Currently pregnant
No 7856 94.0 (93.3–
94.7)
- - 7856 94.0 (93.3–
94.7)
Yes 467 6.0 (5.3–6.7) - - 467 6.0 (5.3–6.7)
No. sex partners in past 12 months
0–1 13825 93.9 (93.4–
94.4)
5652 87.9 (86.9– 88.9)
8173 98.4 (98.0– 98.8)
2+ 890 6.1 (5.6–6.6) 766 12.1 (11.1–
13.1)
124 1.6 (1.2–2.0)
Ever used a condom
No 8998 69.5 (68.1–
70.8)
3566 63.8 (62.0– 65.5)
5432 73.6 (71.9– 75.4)
Yes 3920 30.5 (29.2–
31.9)
1996 36.2 (34.5– 38.0)
1924 26.4 (24.6– 28.1)
Condom use at last sex in past 12 months
Used a condom 1284 9.9 (9.1–
10.6)
810 14.7 (13.5– 15.9)
474 6.3 (5.5–7.1)
Did not use a condom 11634 90.1 (89.4–
90.9)
4752 85.3 (84.1– 86.5)
6882 93.7 (92.9– 94.5)
Unprotected sex at last sex with partner of unknown of discordant HIV status in past 12 months
No 1457 13.6 (12.5–
14.6)
856 17.7 (16.2– 19.2)
601 10.3 (9.1– 11.4)
Yes 9277 86.4 (85.4–
87.5)
3953 82.3 (80.8– 83.8)
5324 89.7 (88.6– 90.9)
Ever tested for HIV
No 9652 67.1 (65.7–
68.5)
4774 75.8 (74.1– 77.5)
4878 60.4 (58.8– 62.0)
Yes 4755 32.9 (31.5–
34.3)
1560 24.2 (22.5– 25.9)
3195 39.6 (38.0– 41.2)
receiving their HIV-negative test result within the last 12 months. The median CD4+ T-cell count in this group was 1,108 cells/mm3; 96.9% were married, and 38.2% were women, of whom 28.1% self-reported being pregnant at the time of the survey, and 41.8% had last tested for HIV infection as part of antenatal care within the year preceding the survey.
Risk factors for recent and long-standing HIV infection
In bivariate analysis, residence in Nairobi, Nyanza, and Rift Valley provinces; being currently married or widowed; reporting any syndrome or diagnosis of sexually transmitted infections (STI) in the past 12 months; and reporting a genital ulcer (men only) were significantly associ-ated with increased odds of recent infection and considered potential risk factors for recent Table 2. (Continued)
HIV-uninfected
Total (N = 14,772) Men (N = 6,449) Women (N = 8,323)
N % (95% CI) N % (95% CI) N % (95% CI)
Self-reported HIV status based on last HIV test result
HIV-positive 18 0.1 (0.1–0.2) 5 0.1 (0–0.1) 13 0.1 (0.1–0.2)
HIV-negative 4576 32.0 (30.6–
33.4)
1482 23.2 (21.4– 24.9)
3094 38.7 (37.1– 40.4)
Unknown, never tested 9652 67.9 (66.5–
69.3)
4774 76.8 (75.0– 78.5)
4878 61.1 (59.5– 62.7)
Reported sexually transmitted infection diagnosis or symptoms in past 12 months
No 14405 97.4 (97.1–
97.7)
6219 96.5 (96.0– 97.0)
8186 98.1 (97.6– 98.5)
Yes 367 2.6 (2.3–2.9) 230 3.5 (3.0–4.0) 137 1.9 (1.5–2.4)
HSV-2 result
Negative 10151 68.4 (67.3–
69.5)
4944 76.4 (75.0– 77.8)
5207 62.2 (60.8– 63.6)
Positive 4479 31.6 (30.5–
32.7)
1455 23.6 (22.2– 25.0)
3024 37.8 (36.4– 39.2)
Syphilis result
Negative 14391 98.4 (98.1–
98.7)
6294 98.4 (98.0– 98.8)
8097 98.4 (98.1– 98.8)
Positive 222 1.6 (1.3–1.9) 97 1.6 (1.2–2.0) 125 1.6 (1.2–1.9)
Genital sore or ulcer on or near the penis in past 12 months
No 6359 98.6 (98.3–
99.0)
6359 98.6 (98.3– 99.0)
-
-Yes 90 1.4 (1.0–1.7) 90 1.4 (1.0–1.7) -
-Male circumcision status
Circumcised 854 14.1 (12.3–
15.9)
854 14.1 (12.3– 15.9)
-
-Not circumcised 5595 85.9 (84.1–
87.7)
5595 85.9 (84.1– 87.7)
-
-1Secondary+ includes any years of secondary schooling whether completed or not.
2The wealth index was a composite measure of the living standard of a household, calculated using data on a household
’s ownership of selected assets, materials used for houshing construction, water access and sanitation facitlities. The wealth index placed household on a contniuous scale of relative weath using principal components anlysis. Individuals were ranked according to the score of the household in which they resided and the sample was divisded intofive groups, each with an equal number of indiviuals (quintiles), ranging from the lowest to highest level of wealth.
infection (data not shown). After adjusting for demographics, sexual behavior, and biological indicators, factors independently and significantly associated with higher adjusted odds of recent infection, compared to the HIV-uninfected control population, included living in Nai-robi (AOR 11.37; CI 2.64–48.87) and Nyanza (AOR 4.55; CI 1.39–14.89) provinces compared with Western province; being widowed (AOR 8.04; CI 1.42–45.50) or currently married (AOR 6.42; CI 1.55–26.58) compared to being never married; having two or more sexual partners in the past 12 months (AOR 2.86; CI 1.51–5.41); not using a condom at last sex in the past 12 months (AOR 1.61; CI 1.34–1.93); reporting STI diagnosis or symptoms of STI in the past 12 months (AOR 1.97; CI 1.05–8.37); and being younger than aged 30 years with the following characteristics: 1) having laboratory-confirmed HSV-2 infection (AOR 8.84; CI 2.62–29.85), 2) being an uncircumcised male (AOR 17.83; CI 2.19–144.90), or 3) having a male genital ulcer (AOR 8.70; CI 2.36–32.08) (Table 3).
In contrast to risk factors for recent infection, compared to HIV-uninfected persons, factors that were independently and significantly associated with increased adjusted odds of long-standing infection were living in Nyanza (AOR 2.33; CI 1.67–3.25) and Coast (AOR 1.55; CI 1.04–2.32) provinces compared to Western province; being separated/divorced (AOR 1.87; CI 1.16–3.01) or widowed (AOR 2.83; CI 1.78–4.45) compared to being never married; having ever used a condom (AOR 1.61; CI 1.34–1.93); and having had a STI diagnosis or STI syn-drome in the past 12 months (AOR 1.89; CI 1.20–2.97). Using a condom at last sex in the past 12 months (AOR 0.47; CI 0.36–0.61); being aged<30 years and HSV-2 negative (AOR 0.38; CI 0.20–0.75);; and being an uncircumcised male aged<30 years (AOR 0.30; CI 0.15–0.61) was associated with lower adjusted odds of long-standing infection compared to their counterparts.
Direct comparison of recent and long-standing infection
In the model that directly compared recent with long-standing infection, persons with recent infection had significantly higher adjusted odds of living in Nairobi province (AOR 8.46; CI 1.28–55.96) compared to Western province; being currently married (AOR 4.64; CI 1.17–
18.37) compared to never being married; not using a condom at last sex in the past 12 months (AOR 5.07; CI 2.06–12.48); having two or more sexual partners in the past 12 months (AOR 3.93; CI 1.49–10.35); and being aged<30 years with 1) laboratory-confirmed HSV-2 infection (AOR15.16; CI 4.50–51.03), 2) male genital ulcer (AOR 7.51; CI 2.25–25.11), or 3) being an uncircumcised male (AOR 90.44; CI 7.64–1070.80) compared to persons aged 30 years and older.
Discussion
The application of a LAg-based RITA in a nationally representative population-based HIV serologic survey in Kenya provided an opportunity to identify risk factors for recently acquired HIV infection, offering empirical evidence for where HIV prevention strategies should target for greater impact on the HIV epidemic. We found significant demographic, behavioral, and biological differences between recent and long-standing infection, supporting the utility of RITAs to distinguish between factors that are associated with long-standing HIV infection with those that are associated with recent infection. We also observed immunological differ-ences between recent and long-standing infection which further support the robustness of this approach.
Table 3. Factors associated with recent and long-term infection among persons aged 15–64 years in
Kenya, 2007 Kenya AIDS Indicator Survey.
Recent infection compared to HIV
uninfected
Long-term infection compared to HIV
uninfected Adjusted OR1,2 Adjusted OR2,3
(95% CI) (95% CI)
Select variables
Sex
Men 1.0 1.0
Women 1.35 (0.46–4.00) 0.99 (0.63–1.54)
Education
No primary 1.0 1.0
Incomplete primary 1.14 (0.42–3.07) 1.42 (1.00–2.02)
Complete primary 0.41 (0.13–1.26) 1.29 (0.88–1.88)
Secondary+4 0.41 (0.13
–1.30) 1.11 (0.74–1.66)
Residence
Urban 1.0 1.0
Rural 1.54 (0.60–3.92) 1.01 (0.66–1.54)
Province
Nairobi 11.37 (2.64–48.87) 1.76 (0.99–3.12)
Central 1.80 (0.43–7.58) 0.79 (0.45–1.37)
Coast 2.31 (0.62–8.66) 1.55 (1.04–2.32)
Eastern 0.78 (0.16–3.89) 1.03 (0.67–1.57)
North Eastern 0.31 (0.03–3.16) 0.86 (0.34–2.19)
Nyanza 4.55 (1.39–14.89) 2.33 (1.67–3.25)
Rift Valley 3.35 (0.93–12.07) 1.31 (0.85–2.04)
Western 1.0 1.0
Marital Status
Never married/cohabiting 1.0 1.0
Separated/divorced 2.03 (0.30–13.59) 1.87 (1.16–3.01)
Widowed 8.04 (1.42–45.50) 2.83 (1.78–4.45)
Currently married/cohabiting 6.42 (1.55–26.58) 1.07 (0.73–1.58)
No. sex partners in past 12 months
0–1 1.0 1.0
2+ 2.86 (1.51–5.41) 0.94 (0.69–1.27)
Ever used a condom
No 1.0 1.0
Yes 0.63 (0.31–1.30) 1.61 (1.34–1.93)
Condom use at last sex with partner in past 12 months
Used a condom 1.0 1.0
Did not use a condom 1.61 (1.34–1.93) 0.47 (0.36–0.61)
Reported STI diagnosis or symptoms in past 12 months
No 1.0 1.0
Yes 1.97 (1.05–8.37) 1.89 (1.20–2.97)
Age category (<30 years vs.30 years) and HSV2 infection status (HSV-2 negative vs. HSV-2 positive)
Aged<30 years/HSV-2 negative 1.56 (0.39–6.28) 0.38 (0.20–0.75)
associated with both recent and long-standing infection. Yet differences were observed in the magnitude of association, with the odds of recent infection over two times that of long-stand-ing infection for widowed persons.
In 2007, the risk of recent infection was highest in the two regions in the country with the largest burden of HIV infection: Nairobi and Nyanza provinces. These provinces accounted for 43% of adults living with HIV in the country, the majority of whom were unaware of their infection [20]. Nearly a decade later, the HIV epidemic has continued to severely afflict these two provinces, which were home to the top five counties with the highest burden of HIV cases in the country in 2015 [28]. Given the high level of unrecognized transmission occurring in these regions, strategies to substantially reduce the infectiousness of individuals is an essential prevention strategy to decrease transmission risks in these settings. By targeting HIV testing in geographic regions and sub-populations that will yield higher numbers of HIV-positive per-sons, treating these individuals, and ensuring that they adhere to their HIV medications, viral suppression can be achieved. Understanding the unique demographic and epidemiological nuances in each of these regions will be important to determine how these prevention and treatment strategies should be designed and where they should be delivered for greatest impact.
Beyond geographical insights, our results corroborate the importance of behavioral and bio-logical factors that contribute concurrently to transmission of HIV infection. Persons reporting multiple sex partners in the past year were more likely to have recent infection than those who did not. Additionally, though not surprisingly, lack of condom use at last sexual intercourse Table 3. (Continued)
Recent infection compared to HIV
uninfected
Long-term infection compared to HIV
uninfected Adjusted OR1,2 Adjusted OR2,3
(95% CI) (95% CI)
Aged<30 years/HSV-2 positive 8.84 (2.62–29.85) 0.59 (0.33–1.06)
Age category (<30 years vs.30 years) and circumcision status (circumcised vs. uncircumcised)
Aged<30 years/circumcised 0.77 (0.24–2.47) 0.76 (0.37–1.55) Aged<30 years/uncircumcised 17.83 (2.19–144.90) 0.30 (0.15–0.61)
Age category (<30 years vs.30 years) and male genital ulcer status (no male genital ulcer vs. male genital ulcer)
Aged<30 years/no male genital ulcer 1.58 (0.10–25.11) 0.24 (0.05–1.05) Aged<30 years/male genital ulcer 8.70 (2.36–32.08) 0.10 (0.55–1.70)
1Specimens were de
fined as being from persons with recent HIV infection if they tested recent on LAg and were not from HIV-infected persons who were on ART. Recent infection was presumed to represent HIV infection acquired less than one year prior to specimen collection.
2A multinomial logistic regression model was applied, controlling for all variables listed in the table. Bolded
estimates reached a significance level of p<0.05 in multinomial logistic regression anlaysis.
3Specimens were de
fined as being from persons with long-standing HIV infection if they: 1) tested non-recent on LAg or 2) tested non-recent on LAg and were from HIV-infected persons who were on ART. Long-standing infection was presumed to represent HIV infection acquired one year or greater prior to specimen collection.
4Secondary+ includes any years of secondary schooling whether completed or not.
was a risk factor for recent HIV infection. In combination, these high-risk behaviors also pro-mote transmission of STI which directly increase the infectiousness of HIV and susceptibility for HIV acquisition [29]. When used correctly and consistently with sexual partners, the effec-tiveness of condoms to prevent sexual transmission of HIV and other STI can be as high as 90–
95% [30]. Behavioral interventions that help to modify sexual behavior through education and building skills on how to correctly and consistently use condoms, particularly for those with multiple sexual partners, can be successful but require populations to be motivated to change their behaviors and sustain these changes [31]. Recognizing that condom use in stable relation-ships is difficult, promotion of condoms should be prioritized for HIV-negative persons who are at greatest risk for new infection, such as those in sexual relationships with partners of HIV-positive or unknown HIV status, and HIV-positive persons. However, given the chal-lenges in relying on behavioral change alone for HIV prevention, behavioral interventions should be delivered in combination with other prevention strategies, including evidenced-based bio-medical, structural, and motivational strategies to strengthen the impact of HIV pre-vention on a larger population level [31].
Our findings also highlight the influence of sociological aspects on transmission of HIV infection. Widowhood has been previously described as a correlate of HIV infection in Kenya, though likely biased from potential death from AIDS of the deceased partner [32,33]. However this is the first instance where widowhood has been described as being associated with recent infection in Kenya. It is possible that sexual rituals related to widowhood may be facilitating new infections in this population. Widow inheritance or widow cleansing, where a male assumes the social or sexual responsibility of female widow after the death of her husband, is a traditional practice is some regions in Kenya, and the use of condoms in these rituals is dis-couraged [34,35]. Further exploration around sexual practices and cultural expectations of widows is needed to provide more clarity on factors that may be contributing to the spread of HIV infection among widows to provide culturally-sensitive options for risk reduction in this population.
The vast majority of recent infection was detected among individuals who were married. While external sexual partnerships can play an important role in introducing HIV infection into an existing marital relationship, the majority of new infections are likely to be occurring in committed relationships with a partner who comes into the relationship already HIV infected. Given the important contribution of married and cohabiting couples in Kenya’s epidemic, HIV testing and counseling services provided to couples hold great potential for reducing new HIV infections on a population level. Not only will knowledge of one’s personal and partner’s HIV status help to improve risk reduction strategies within partnerships [36,37], it will facilitate linkages into early HIV care and treatment services and programs for prevention of mother-to-child transmission, resulting in substantial reductions in viral load and HIV transmission risk in the population [3]. As of 2012, only one-third of persons who had ever been tested for HIV had tested together with a sexual partner, highlighting an important gap in reaching those who may be at greatest risk for new infection [38]. Increased efforts are needed to improve aware-ness of couples testing options and expanding approaches such as family testing in health facili-ties and home-based HIV testing and counseling in the community.
Younger age, genital ulcerative disease (GUD), and lack of male circumcision have been pre-viously reported as risk factors for incident HIV infection in sub-Saharan Africa [2,4,5,28,39–
Kenyans and highlight the opportunity for bio-medical strategies that prevent GUD, including those caused by HSV-2 infection, to facilitate parallel reductions in incident HIV infections in youth. Results from several clinical trials have demonstrated encouraging progress in the expansion of bio-medical options for prevention of HSV-2 infection. Data from two male cir-cumcision trials in Rakai, Uganda and Orange Farm, South Africa found that male circumci-sion decreased the incidence of HSV-2 infection in men by 28% and 55%, respectively [42,43]. Recent data from the Centre for AIDS Programme of Research in South Africa 004 study found that application of Tenofovir gel before and after sexual intercourse can reduce HSV-2 acquisition by 51% among women [44]. Based on this evidence male circumcision among men and Tenofovir among women, coupled with condoms, may be an important consideration for sexually active youth to reduce the spread of GUD and HSV-2 infection, and by extension, acquisition of HIV infection [45].
Our findings were subject to the following limitations. Though the current standard of anal-ysis for estimating assay-derived HIV incidence includes the use of HIV-1 viral load results in a RITA to reduce the occurrence of false-recent cases, the results of viral load testing of KAIS 2007 samples were suggestive of nucleic acid degradation over time and not included in this analysis [46]. The absence of viral load test results presents an important shortcoming given that low viral loads can be a cause of false-recent misclassification on antibody-based HIV inci-dence assays, such as LAg. Undetectable levels of viral RNA in HIV-infected populations can occur from either sustained ART or survival of a unique subset of persons, known as elite suppressors, with naturally low viral loads in the absence of ART. Although we were able to remove some false-recent misclassification due to low viral load by taking into account expo-sure to ART in the RITA applied, the impact was likely to be minimal given that ART coverage in Kenya was low in 2007 [20]. The larger concern was our inability to remove persons with long-standing infection and naturally low viral loads from our final sample of recent infection. Though rare, the level of elite suppression is expected to higher in older epidemics due to sur-vival advantage of these individuals. Based on population-level data in East Africa, these indi-viduals could account for as high as 10% of the untreated HIV-infected population in the region [47]. The frequency of false-recent misclassification for these individuals has been reported to be as high as 13% on LAg based on previous work [25], and extrapolation to our sample suggests that up to 20% of recent infections could potentially be false-recent cases due to low viral load in the absence of treatment. In addition, given the genetic diversity of HIV-1 subtypes in Kenya, some of these misclassified cases may be further explained by differential specificity of LAg by HIV-1 subtype. Dominate HIV-1 strains in Kenya are clade A virus, which comprise over 50% of HIV infection, and clade D virus which covers roughly 12% to 22% of infections [48–53]. Clade D HIV has shown to have high levels of false-recent misclassi-fication on antibody-based HIV incidence assays due to deferred antibody maturation and rapid antibody decline [25,26]. Previous studies have reported LAg-false-recent rates as high 9% to 16% in clade D HIV compared to 1.3% to 2.7% in clade A HIV. Although we observed significantly higher odds of recent infection in the high HIV burden provinces of Nyanza and Nairobi, given the potential variability in the geographic distribution of HIV-1 subtypes in these provinces, these associations should be interpreted cautiously.
would be revised to approximately 100,000 to 120,000 infections. However, in spite of this adjustment, our estimates continue to align well with previously published estimates of adult HIV incidence based multiple estimation approaches, which ranged from 0.6% to 0.7% of the population in 2007 or 120,000 to 140,000 incident infections [10].
Our findings were also vulnerable to other factors associated with false-recent classification which could have led to dilution of observed associations with recent infection. First, the drugs selected for ARV testing were based on the standardized first- and second-line regimens avail-able in public health facilities in Kenya in 2007. If an individual was on an ART regimen that did not include any of these four drugs, the ARV biomarker would not have been detected. Sec-ond, if adherence to ART was poor, the ARV biomarker may not have been detected given the variable half-lives of the ARV drugs tested. The number of days post ingestion to the ARV drug assay cut-off was 12–28 days for EFV, 8–9 days for NVP, 1.5 days for 3TC, and 1.5–2.5 days for LPV (personal communication, Karen Cohen, University of Cape Town). Third, we relied on self-reported data to confirm ART status for individuals who did not have specimens available for ARV drug testing (33% of HIV-positive specimens with LAg Avidity ODn<1.5). It is possible that some individuals on ART did not disclose ART use during the interview. Due to the small sample size of recent infection in this analysis, only major risk factors were likely to be significantly associated with recent infection. We were also not able to conduct a comprehensive analysis of linked infections among sexual partners which could have provided important insight on whether recent infections were being acquired from spouses or outside partnerships. Finally, we were not able to monitor changes in risk factors over time using data from the second Kenya AIDS Indicator Survey, conducted in 2012, as less than 25 recent infec-tions were identified in that survey [30].
In conclusion, we identified risk factors for recent infection that were epidemiologically plausible and immediately actionable. An accurate RITA that can be applied in a national pop-ulation-based serologic survey has wide public health reach as quick as accurate demonstra-tions of declines in new HIV infection can result in millions of lives saved and reducdemonstra-tions in HIV treatment costs. As combination prevention strategies aimed to dramatically reduce HIV transmission are scaled-up, the ability to track the leading edge of the epidemic with reliable information on where and among whom new infections are occurring is more relevant than ever. Given the potential for these data to make a large impact on the HIV epidemic, the appli-cation of a RITA in national HIV surveys with the ability to identify false-recent cases through measurement of viral load and ART use, should be routinely considered, where feasible, to identify risk factors for recent infection.
Supporting Information
S1 File. KAIS Analysis and Publication Concept Sheet Template.
(DOCX)
Acknowledgments
Mwangi, Carol Ngare, Raymond Nyoka, Linus Odawo, Samuel Ogola, Christopher Omolo, Tom Oluoch, Ray Shiraishi, John Wanyungu, Anthony Waruru.
A portion of this work was presented at the 19thConference on Retroviruses and Opportu-nistic Infections, Seattle, Washington, March 5–9, 2012.
Disclaimer: The findings and conclusions in this report are those of the authors and do not necessarily represent the official views of the US Centers for Disease Control and Prevention or other host institutions.
Author Contributions
Conceived and designed the experiments: AAK BSP RB JM. Performed the experiments: AAK BSP MU TG TD. Analyzed the data: AAK BSP RB JM. Contributed reagents/materials/analysis tools: BSP TD MU TG. Wrote the paper: AAK BSP MU TG RB EM TD PM NM KDC JM.
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